The movement of the human eye can reveal a wealth of information about the neural mechanisms of the brain and vision, as well as an individual's neurological health, ocular health, interests, and state-of-mind. In addition, the tracking of eye movement can be used to improve/augment human-computer interaction, enable gaze-based man-machine interfaces, and enhance how we interact with wearable technology. For example, gaze tracking, which relies on eye tracking, enables many augmentative alternative communication (AAC) devices that improve the ability for individuals lacking speech capability and/or motor skills (e.g., amyotrophic lateral sclerosis (ALS) patients or those with spinal cord injuries) to interact with the world around them.
In recent years, eye-tracking technology has become more sophisticated and is increasingly being directed toward non-health-related uses, such as improving advertising effectiveness, determining optimal product placement, improving package design, augmenting the automotive driving experience, gaming and virtual reality (VR) systems, military training and effectiveness augmentation, athletic training, and the like. For advertising and/or product placement applications, for example, the activity of a subject's eyes is tracked while target stimuli (e.g., web sites, commercials, magazine ads, newspapers, product packages, etc.) are presented. The recorded eye-tracking data is statistically analyzed and graphically rendered to reveal specific visual patterns. By examining fixations, saccades, pupil dilation, blinks and a variety of other behaviors, the effectiveness of a given medium or product can be determined.
Helmet-integrated eye-trackers could potentially improve the ability of a fighter pilot to better control an aircraft and react to high-speed battle conditions while in flight. Eye tracking in this scenario may provide capabilities to enhance interaction between the pilot and vehicle. In addition, post-flight analysis of the pilot's gaze could be useful for training purposes. Further, in-flight health monitoring is possible by tracking changes in the geometry of the eye due to swelling caused by rapid changes in pressure.
Unfortunately, conventional eye-trackers are slow, bulky, invasive, and/or restrictive for the user. This makes them difficult, if not impossible to use in many of the applications discussed above. In addition, conventional systems generally require cameras and imaging processing software. As a result, they tend to be expensive, slow, and power hungry. Furthermore, they typically exhibit significant lag between eye movement and measured eye position, which degrades the user experience in VR applications. It is possible to improve the resolution and speed of such eye tracking systems but, to date, these improvements have come at the expense of user mobility and added system cost.
Eye coils mounted directly on the eye have been used to track eye position with high accuracy; however, they require operation within a magnetic field, which restricts the applications in which they can be used. Further, since they are placed directly on the eyeball, they can only be used for limited times due to eye safety and vision impairment issues.
Electro-oculograms enable the position of the cornea through closed eyelids; however, they are subject to blink artifacts and signal noise. In addition, they are relatively inaccurate. Further, they require electrodes to be attached in or near the eye. As a result, electro-oculograms are relatively unattractive in many applications.
Systems for tracking the limbus (i.e., the boundary between the white of the eye and the dark iris) have been used for eye tracking as well. Unfortunately, such limbal-tracking systems are cumbersome, difficult to align and calibrate, and have poor sensitivity unless used at short range.
Perhaps the most common eye-tracking systems are video-based systems, such as image-processing systems described in U.S. Pat. No. 8,955,973. In these systems, a picture of the surface of the eye is taken under infrared (IR) illumination and captured by a two-dimensional image sensor. The picture reveals the location of the corneal reflection and the pupil of the eye. Using complex image processing, a vector from the center of the corneal reflection to the center of the pupil is calculated and this vector forms the basis of an estimate of the direction of the user's gaze. Video-based eye trackers can be used remotely or worn by the subject.
Unfortunately, such eye-tracking systems are slow, bulky, restrictive for the user, and expensive. Wearable systems are bulky and heavy, making them quite uncomfortable for extended use. Remote systems require careful positioning and alignment, which can be easily disrupted during operation. In addition, the reliance on video capture and image processing leads to the need for good lighting conditions. Further, video capture is difficult to perform tracking through spectacles due to front surface reflections.
As an alternative to video-based systems, some conventional eye-tracking systems project a grid of structured light onto the surface of the eye. Unfortunately, an image of the eye surface must still be captured and analyzed via image processing to estimate eye position. As a result, while such systems typically require less computational complexity than video-based eye-trackers, they still demand significant computational and energy resources.
A low-cost, high resolution, low power, high-speed, robust eye-tracking system would, therefore, be a significant advance in the state of the art.